Infrared thermography captures real-time degradation temperature information, facilitating noncontact machine health monitoring. However, the inherent multiscale characteristics and spatiotemporal degradation discrepancy in infrared images pose a challenge in learning discriminative degradation features and adaptive prognostic analytics. This article presents a sparse hierarchical parallel residual networks ensemble (SHPRNE) method to tackle this challenge. First, the hierarchical parallel residual network (HPRN) leverages parallel multiscale kernels to capture complementary degradation patterns separately and embeds a hierarchical residual connection procedure to facilitate the interactivity between coarse-to-fine level features. Moreover, SHPRNE develops a sparse ensemble algorithm integrated with a synergy of network pruning and local minima perpetuation to derive diverse HPRNs while alleviating the parameter storage budget. Pruned HPRNs with varying sparsity and local minima are further integrated into an ensemble learner with higher generalization. Case studies on two infrared image datasets are conducted to demonstrate the effectiveness and superiority of the proposed method.

Infrared thermography provides abundant spatiotemporal degradation information, facilitating non-contact condition monitoring. Reducing domain shift between simulated and industrial infrared images is significantly desired for leveraging labeled simulated data to tackle practical insufficiency of run-to-failure samples. Recently, adversarial-based domain adaptation (DA) techniques have aroused broad concern in solving domain shifts. However, simultaneously aligning marginal and conditional distributions in cross-domain remaining useful life (RUL) prediction is rarely researched in adversarial-based DA. In this article, an adversarial regressive domain adaptation (ARDA) approach is, thus, put forward to address this challenge. First, a regressive disparity discrepancy is designed to describe the dissimilarity between distributions and derive the generalization bound for cross-domain prognostics. Guided by this bound, the ARDA effectively aligns marginal and conditional distributions by learning indistinguishable features and considering the relationship between samples and prediction tasks. Simulated and experimental infrared degradation image datasets are used to demonstrate the effectiveness and superiority of the proposed approach over existing methods for cross-domain RUL prediction.

In this paper, two criterions of MDS Euclidean self-orthogonal codes are presented. New MDS Euclidean self-dual codes and self-orthogonal codes are constructed via the criterions. In particular, among our constructions, for large square q, about 1/8·q new MDS Euclidean (almost) self-dual codes over Fqcan be produced. Moreover, we can construct about 1/4· q new MDS Euclidean self-orthogonal codes with different even lengths n and dimension n/2-1.